code: def test_augmentation_param_hyper_model():
      class HyperAug(hm_module.HyperModel):
          def build(self, hp):
              model = keras.Sequential(name="aug")
              scaling_factor = hp.Choice("scaling_factor", [1])
              model.add(
                  keras.layers.Lambda(lambda x: x * scaling_factor)
              )
              return model
  
      hp = hp_module.HyperParameters()
      aug_hm = HyperAug()
      hypermodel = efficientnet.HyperEfficientNet(
          input_shape=INPUT_SHAPE_32,
          classes=10,
          augmentation_model=aug_hm,
      )
      model = hypermodel.build(hp)
      assert model.layers[1].name == "aug"
      assert hp.values["scaling_factor"] == 1
  
file_path: keras-tuner/keras_tuner/applications/efficientnet_test.py
imports: import numpy
  import pytest
  from keras_tuner.applications import efficientnet
  from keras_tuner.backend import config
  from keras_tuner.backend import keras
  from keras_tuner.engine import hypermodel
  from keras_tuner.engine import hyperparameters
label_desc: Use image node_id 8 for calling a global function with example usage: test_augmentation_param_hyper_model() and returns: model